{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the [CartPole-v1](https://gym.openai.com/envs/CartPole-v0/) OpenAI Gym environment. For reproducibility, let is fix a random seed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:39.238680Z",
     "start_time": "2021-02-25T18:41:36.874709Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104138.043 dataclasses.py:48] USE_VANILLA_DATACLASS: True\n",
      "I0225 104138.045 dataclasses.py:49] ARBITRARY_TYPES_ALLOWED: True\n",
      "W0225 104138.056 file_io.py:72] ** fvcore version of PathManager will be deprecated soon. **\n",
      "** Please migrate to the version in iopath repo. **\n",
      "https://github.com/facebookresearch/iopath \n",
      "\n",
      "W0225 104138.062 manifold.py:86] ** fvcore version of PathManager will be deprecated soon. **\n",
      "** Please migrate to iopath. **\n",
      "\n",
      "I0225 104138.064 io.py:19] Registered Manifold PathManager\n",
      "W0225 104138.068 manifold.py:86] ** fvcore version of PathManager will be deprecated soon. **\n",
      "** Please migrate to iopath. **\n",
      "\n",
      "I0225 104138.069 patch.py:95] Patched torch.load, torch.save, torch.jit.load and save to handle Manifold uri\n",
      "I0225 104138.232 registry_meta.py:19] Adding REGISTRY to type TrainingReport\n",
      "I0225 104138.233 registry_meta.py:40] Not Registering TrainingReport to TrainingReport. Abstract method [] are not implemented.\n",
      "I0225 104138.234 registry_meta.py:19] Adding REGISTRY to type PublishingResult\n",
      "I0225 104138.234 registry_meta.py:40] Not Registering PublishingResult to PublishingResult. Abstract method [] are not implemented.\n",
      "I0225 104138.235 registry_meta.py:19] Adding REGISTRY to type ValidationResult\n",
      "I0225 104138.236 registry_meta.py:40] Not Registering ValidationResult to ValidationResult. Abstract method [] are not implemented.\n",
      "I0225 104138.237 registry_meta.py:31] Registering NoPublishingResults to PublishingResult\n",
      "I0225 104138.238 registry_meta.py:34] Using no_publishing_results instead of NoPublishingResults\n",
      "I0225 104138.239 registry_meta.py:31] Registering NoValidationResults to ValidationResult\n",
      "I0225 104138.239 registry_meta.py:34] Using no_validation_results instead of NoValidationResults\n",
      "I0225 104138.244 registry_meta.py:31] Registering SchedulingFrequencyValidationResults to ValidationResult\n",
      "I0225 104138.245 registry_meta.py:34] Using scheduling_frequency_validation_results instead of SchedulingFrequencyValidationResults\n",
      "I0225 104138.247 registry_meta.py:31] Registering PDIVFilterValidationResults to ValidationResult\n",
      "I0225 104138.247 registry_meta.py:34] Using pdiv_filter_validation_results instead of PDIVFilterValidationResults\n",
      "I0225 104138.249 registry_meta.py:31] Registering Seq2SlateValidationResults to ValidationResult\n",
      "I0225 104138.249 registry_meta.py:34] Using seq2slate_validation_results instead of Seq2SlateValidationResults\n",
      "I0225 104138.250 registry_meta.py:31] Registering SchedulingFrequencyPublishingResults to PublishingResult\n",
      "I0225 104138.251 registry_meta.py:34] Using scheduling_frequency_publishing_results instead of SchedulingFrequencyPublishingResults\n",
      "I0225 104138.252 registry_meta.py:31] Registering PDIVFilterPublishingResults to PublishingResult\n",
      "I0225 104138.253 registry_meta.py:34] Using pdiv_filter_publishing_results instead of PDIVFilterPublishingResults\n",
      "I0225 104138.254 registry_meta.py:31] Registering FeedPublishingResults to PublishingResult\n",
      "I0225 104138.255 registry_meta.py:34] Using feed_publishing_results instead of FeedPublishingResults\n",
      "I0225 104138.256 registry_meta.py:31] Registering ScoreFblearnerPredictorPublishingResult to PublishingResult\n",
      "I0225 104138.257 registry_meta.py:34] Using score_offline_results instead of ScoreFblearnerPredictorPublishingResult\n",
      "I0225 104138.258 registry_meta.py:31] Registering ScoreSeq2SlateOutput to PublishingResult\n",
      "I0225 104138.259 registry_meta.py:34] Using score_seq2slate_offline instead of ScoreSeq2SlateOutput\n",
      "I0225 104138.260 registry_meta.py:31] Registering IPSResult to PublishingResult\n",
      "I0225 104138.261 registry_meta.py:34] Using learnvm_ips_result instead of IPSResult\n",
      "I0225 104138.263 registry_meta.py:31] Registering SlateRewardFeatureImportanceOutput to PublishingResult\n",
      "I0225 104138.264 registry_meta.py:34] Using slate_reward_feature_importance instead of SlateRewardFeatureImportanceOutput\n",
      "I0225 104138.268 dataclasses.py:73] Setting IdMapping.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104138.269 dataclasses.py:73] Setting ModelFeatureConfig.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104138.303 registry_meta.py:19] Adding REGISTRY to type LearningRateSchedulerConfig\n",
      "I0225 104138.304 registry_meta.py:40] Not Registering LearningRateSchedulerConfig to LearningRateSchedulerConfig. Abstract method [] are not implemented.\n",
      "I0225 104138.306 registry_meta.py:19] Adding REGISTRY to type OptimizerConfig\n",
      "I0225 104138.306 registry_meta.py:40] Not Registering OptimizerConfig to OptimizerConfig. Abstract method [] are not implemented.\n",
      "I0225 104138.308 registry_meta.py:31] Registering Adam to OptimizerConfig\n",
      "I0225 104138.309 registry_meta.py:31] Registering SGD to OptimizerConfig\n",
      "I0225 104138.311 registry_meta.py:31] Registering AdamW to OptimizerConfig\n",
      "I0225 104138.312 registry_meta.py:31] Registering SparseAdam to OptimizerConfig\n",
      "I0225 104138.314 registry_meta.py:31] Registering Adamax to OptimizerConfig\n",
      "I0225 104138.315 registry_meta.py:31] Registering LBFGS to OptimizerConfig\n",
      "I0225 104138.317 registry_meta.py:31] Registering Rprop to OptimizerConfig\n",
      "I0225 104138.322 registry_meta.py:31] Registering ASGD to OptimizerConfig\n",
      "I0225 104138.324 registry_meta.py:31] Registering Adadelta to OptimizerConfig\n",
      "I0225 104138.325 registry_meta.py:31] Registering Adagrad to OptimizerConfig\n",
      "I0225 104138.327 registry_meta.py:31] Registering RMSprop to OptimizerConfig\n",
      "I0225 104138.343 dataclasses.py:73] Setting Seq2SlateNet.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104138.359 dataclasses.py:73] Setting CRRWeightFn.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104138.380 registry_meta.py:19] Adding REGISTRY to type EnvWrapper\n",
      "I0225 104138.381 registry_meta.py:40] Not Registering EnvWrapper to EnvWrapper. Abstract method ['obs_preprocessor', 'serving_obs_preprocessor', 'make'] are not implemented.\n",
      "I0225 104138.382 dataclasses.py:73] Setting EnvWrapper.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104138.387 registry_meta.py:31] Registering ChangingArms to EnvWrapper\n",
      "I0225 104138.402 registry_meta.py:31] Registering Gym to EnvWrapper\n",
      "I0225 104138.406 utils.py:18] Registering id=Pocman-v0, entry_point=reagent.gym.envs.pomdp.pocman:PocManEnv.\n",
      "I0225 104138.407 utils.py:18] Registering id=StringGame-v0, entry_point=reagent.gym.envs.pomdp.string_game:StringGameEnv.\n",
      "I0225 104138.407 utils.py:18] Registering id=LinearDynamics-v0, entry_point=reagent.gym.envs.dynamics.linear_dynamics:LinDynaEnv.\n",
      "I0225 104138.408 utils.py:18] Registering id=PossibleActionsMaskTester-v0, entry_point=reagent.gym.envs.functionality.possible_actions_mask_tester:PossibleActionsMaskTester.\n",
      "I0225 104138.409 utils.py:18] Registering id=StringGame-v1, entry_point=reagent.gym.envs.pomdp.string_game_v1:StringGameEnvV1.\n",
      "I0225 104138.433 registry_meta.py:31] Registering RecSim to EnvWrapper\n",
      "I0225 104138.435 dataclasses.py:73] Setting RecSim.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104138.437 registry_meta.py:31] Registering OraclePVM to EnvWrapper\n",
      "I0225 104138.437 dataclasses.py:73] Setting OraclePVM.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104138.446 registry_meta.py:31] Registering ToyVM to EnvWrapper\n",
      "\n",
      "Bad key \"axes.color_cycle\" on line 214 in\n",
      "/home/alexnik/.matplotlib/matplotlibrc.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.2/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import pytorch_lightning as pl\n",
    "from reagent.gym.envs.gym import Gym\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import tqdm.autonotebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:39.429693Z",
     "start_time": "2021-02-25T18:41:39.240892Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104139.247 env_wrapper.py:38] Env: <TimeLimit<CartPoleEnv<CartPole-v0>>>;\n",
      "observation_space: Box(4,);\n",
      "action_space: Discrete(2);\n",
      "I0225 104139.250 seed.py:57] Global seed set to 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "bento_obj_id": "139934208915616"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env = Gym('CartPole-v0')\n",
    "env.seed(0)\n",
    "env.action_space.seed(0)\n",
    "pl.seed_everything(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `policy` is composed of a simple scorer (a MLP) and a softmax sampler. Our `agent` simply executes this policy in the CartPole Environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:39.723885Z",
     "start_time": "2021-02-25T18:41:39.432154Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104139.542 registry_meta.py:19] Adding REGISTRY to type DiscreteDQNNetBuilder\n",
      "I0225 104139.543 registry_meta.py:40] Not Registering DiscreteDQNNetBuilder to DiscreteDQNNetBuilder. Abstract method ['build_q_network'] are not implemented.\n",
      "I0225 104139.543 registry_meta.py:31] Registering Dueling to DiscreteDQNNetBuilder\n",
      "I0225 104139.544 dataclasses.py:73] Setting Dueling.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104139.546 registry_meta.py:31] Registering FullyConnected to DiscreteDQNNetBuilder\n",
      "I0225 104139.547 dataclasses.py:73] Setting FullyConnected.__post_init__ to its __post_init_post_parse__\n",
      "I0225 104139.548 registry_meta.py:31] Registering FullyConnectedWithEmbedding to DiscreteDQNNetBuilder\n",
      "I0225 104139.549 dataclasses.py:73] Setting FullyConnectedWithEmbedding.__post_init__ to its __post_init_post_parse__\n"
     ]
    }
   ],
   "source": [
    "from reagent.net_builder.discrete_dqn.fully_connected import FullyConnected\n",
    "from reagent.gym.utils import build_normalizer\n",
    "\n",
    "norm = build_normalizer(env)\n",
    "net_builder = FullyConnected(sizes=[8], activations=[\"linear\"])\n",
    "cartpole_scorer = net_builder.build_q_network(\n",
    "    state_feature_config=None, \n",
    "    state_normalization_data=norm['state'],\n",
    "    output_dim=len(norm['action'].dense_normalization_parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:39.905841Z",
     "start_time": "2021-02-25T18:41:39.726095Z"
    }
   },
   "outputs": [],
   "source": [
    "from reagent.gym.policies.policy import Policy\n",
    "from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler\n",
    "from reagent.gym.agents.agent import Agent\n",
    "\n",
    "\n",
    "policy = Policy(scorer=cartpole_scorer, sampler=SoftmaxActionSampler())\n",
    "agent = Agent.create_for_env(env, policy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a trainer that uses the REINFORCE Algorithm to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:40.079237Z",
     "start_time": "2021-02-25T18:41:39.907857Z"
    }
   },
   "outputs": [],
   "source": [
    "from reagent.training.reinforce_trainer import ReinforceTrainer\n",
    "from reagent.optimizer.union import classes\n",
    "\n",
    "\n",
    "reinforce_trainer = ReinforceTrainer(\n",
    "    policy=policy,\n",
    "    gamma=0.99,\n",
    "    optimizer=classes['Adam'](lr=5e-3, weight_decay=1e-3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RL Interaction Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:44.651922Z",
     "start_time": "2021-02-25T18:41:40.081054Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104142.407 gymrunner.py:132] For gamma=1.0, average reward is 18.6\n",
      "Rewards list: [15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18.\n",
      " 15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18. 15. 18.\n",
      " 15. 18. 15. 18. 29. 12. 29. 12. 29. 12. 29. 12. 29. 12. 29. 12. 29. 12.\n",
      " 29. 12. 29. 12. 29. 12. 29. 12. 29. 12. 29. 12. 29. 12. 29. 12. 29. 12.\n",
      " 29. 12. 17. 21. 29. 12. 29. 12. 17. 21. 17. 21. 29. 12. 17. 21. 17. 21.\n",
      " 17. 21. 17. 21. 17. 21. 17. 21. 17. 21.]\n"
     ]
    }
   ],
   "source": [
    "from reagent.gym.runners.gymrunner import evaluate_for_n_episodes\n",
    "eval_rewards = evaluate_for_n_episodes(100, env, agent, 500, num_processes=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run training loop (managed by Pytorch Lightning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:44.832445Z",
     "start_time": "2021-02-25T18:41:44.654204Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104144.656 seed.py:57] Global seed set to 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "bento_obj_id": "139934208915616"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.seed_everything(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:45.015184Z",
     "start_time": "2021-02-25T18:41:44.834628Z"
    }
   },
   "outputs": [],
   "source": [
    "num_episodes = 175\n",
    "reward_min = 20\n",
    "max_steps = 200\n",
    "reward_decay = 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:41:45.206743Z",
     "start_time": "2021-02-25T18:41:45.018149Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104145.027 distributed.py:54] GPU available: False, used: False\n",
      "I0225 104145.029 distributed.py:54] TPU available: None, using: 0 TPU cores\n"
     ]
    }
   ],
   "source": [
    "from reagent.gym.datasets.episodic_dataset import EpisodicDataset, EpisodicDatasetDataloader\n",
    "\n",
    "pl_trainer = pl.Trainer(max_epochs=1, deterministic=True)\n",
    "dataset = EpisodicDataset(env=env, agent=agent, num_episodes=num_episodes, seed=0, max_steps=max_steps)\n",
    "\n",
    "train_rewards = []\n",
    "class TrainRewardsExtractor(EpisodicDataset):\n",
    "    # a wrapper around a dataset to enable logging of rewards during training\n",
    "    def __init__(self, dataset):\n",
    "        self.dataset = dataset\n",
    "        \n",
    "    def __iter__(self):\n",
    "        for traj in iter(self.dataset):\n",
    "            ep_reward = traj[\"reward\"].sum().item()\n",
    "            train_rewards.append(ep_reward)\n",
    "            yield traj\n",
    "            \n",
    "    def __getattr__(self, name):\n",
    "        return getattr(self.dataset, name)\n",
    "    \n",
    "dataset = TrainRewardsExtractor(dataset)\n",
    "\n",
    "dataloader = EpisodicDatasetDataloader(dataset, num_episodes_between_updates=1, batch_size=1, num_epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:42:15.446538Z",
     "start_time": "2021-02-25T18:41:45.209061Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104145.227 lightning.py:1381] \n",
      "  | Name   | Type              | Params\n",
      "---------------------------------------------\n",
      "0 | scorer | FullyConnectedDQN | 58    \n",
      "---------------------------------------------\n",
      "58        Trainable params\n",
      "0         Non-trainable params\n",
      "58        Total params\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 175/175 [00:30<00:00,  5.83it/s, loss=-0.075, v_num=3] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "bento_obj_id": "139934208915648"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl_trainer.fit(reinforce_trainer, dataloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the rewards over training episodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:42:16.328382Z",
     "start_time": "2021-02-25T18:42:15.448696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x720 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x7f447806d8d0>)"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "bento_obj_id": "139927970772224"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "139932048217936"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_rewards(rewards):\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(12, 10));\n",
    "    pd.Series(rewards).rolling(50).mean().plot(ax=ax);\n",
    "    pd.Series(rewards).plot(ax=ax,alpha=0.5,color='lightblue');\n",
    "    ax.set_xlabel('Episodes');\n",
    "    ax.set_ylabel('Reward');\n",
    "    plt.title('REINFORCE on CartPole');\n",
    "    plt.legend(['Moving Average Reward', 'Instantaneous Episode Reward'])\n",
    "    return fig, ax\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "sns.set()\n",
    "\n",
    "\n",
    "plot_rewards(train_rewards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print eval rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-25T18:43:32.330306Z",
     "start_time": "2021-02-25T18:42:16.331040Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0225 104332.151 gymrunner.py:132] For gamma=1.0, average reward is 198.59\n",
      "Rewards list: [200. 200. 200. 200. 200. 200. 200. 167. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 100. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 191. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 170. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 151. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 190.\n",
      " 200. 200. 149. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200. 200.\n",
      " 200. 200. 200. 200.]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean reward: 198.59\n"
     ]
    }
   ],
   "source": [
    "eval_episodes = 200\n",
    "eval_rewards = evaluate_for_n_episodes(eval_episodes, env, agent, 500, num_processes=1).T[0]\n",
    "mean_reward = pd.Series(eval_rewards).mean()\n",
    "print(f'Mean reward: {mean_reward:.2f}')"
   ]
  }
 ],
 "metadata": {
  "anp_cloned_from": {
   "revision_id": "351369499371280"
  },
  "bento_stylesheets": {
   "bento/extensions/flow/main.css": true,
   "bento/extensions/kernel_selector/main.css": true,
   "bento/extensions/kernel_ui/main.css": true,
   "bento/extensions/new_kernel/main.css": true,
   "bento/extensions/system_usage/main.css": true,
   "bento/extensions/theme/main.css": true
  },
  "kernelspec": {
   "display_name": "alexnik (local)",
   "language": "python",
   "name": "alexnik_local"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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